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In this paper, we propose a generalization for fuzzy graphs in order to model reactive systems with fuzziness. As we will show, the resulting fuzzy structure, called fuzzy reactive graphs (FRG), is able to model dynamical aspects of some entities which generally appear in: biology, computer science and some other fields. The dynamical aspect is captured by a transition function which updates the values of the graph after an edge has been crossed. The update process takes into account aggregation functions. The paper proposes a notion for bisimulation for such graphs and briefly shows how modal logic can be used to verify properties of systems modeled with FSGs. The paper closes with a toy example in the field of Biology. published

Data-driven approaches are becoming dominant in almost every single software engineering activity, and requirements engineering is not the exception. The analysis of data coming from several sources may indeed become an extremely useful input to requirements elicitation and management. However, benefits do not come for free. Techniques such as natural language processing and machine learning are difficult to master and require high-quality data and specific competences from different fields, whilst their generalization remains as a challenge. This paper introduces the main concepts behind data-driven requirements engineering, provides an overview of the state of the art in the field and identifies the main challenges to be addressed. This work is partially supported by the GENESIS project, funded by the Spanish Ministerio de Ciencia e Innovación under contract TIN2016-79269-R.

The Anscombe-Aumann (AA) model, originally introduced to give a normative basis to expected utility, is nowadays mostly used for another purpose: to analyze deviations from expected utility due to ambiguity (unknown probabilities). The AA model makes two ancillary assumptions that do not refer to ambiguity: expected utility for risk and backward induction. These assumptions, even if normatively appropriate, fail descriptively. This paper relaxes these ancillary assumptions to avoid the descriptive violations, while maintaining AA’s convenient mixture operation. Thus, it becomes possible to test and apply all AA-based ambiguity theories descriptively while avoiding confounds due to violated ancillary assumptions. The resulting tests use only simple stimuli, avoiding noise due to complexity. We demonstrate the latter in a simple experiment where we find that three assumptions about ambiguity, commonly made in AA theories, are violated: reference independence, universal ambiguity aversion, and weak certainty independence. The second, theoretical, part of the paper accommodates the violations found for the first ambiguity theory in the AA model—Schmeidler’s CEU theory—by introducing and axiomatizing a reference dependent generalization. That is, we extend the AA ambiguity model to prospect theory.

The aim of this paper is to study certain problems of calculus of variations, that are dependent upon a Lagrange function on a Caputo-type fractional derivative. This type of fractional operator is a generalization of the Caputo and the Caputo--Hadamard fractional derivatives, that are dependent on a real parameter ro. Sufficient and necessary conditions of the first and second order are presented. The cases of integral and holonomic constraints are also considered., Comment: This is a preprint of a paper whose final and definite form will be published in Journal of Optimization Theory and Applications

In this paper, we study a very general type of online network design problem, and generalize two different previous algorithms, one for an online network design problem due to Berman and Coulston [4] and one for (offline) general network design problems due to Goemans and Williamson [9]; we give an O(log k)-competitive algorithm, where k is the number of nodes that must be connected. We also consider a further generalization of the problem that allows us to pay penalties in exchange for violating connectivity constraints; we give an online O(log k)-competitive algorithm for this case as well., A preliminary version [20] appeared in ICALP 2011 but was later discovered to be flawed. This paper presents a corrected version of the algorithm and proofs, and a generalisation of the prize-collecting problem considered in [20]

Multilabel classification (ML) aims to assign a set of labels to an instance. This generalization of multiclass classification yields to the redefinition of loss functions and the learning tasks become harder. The objective of this paper is to gain insights into the relations of optimization aims and some of the most popular performance measures: subset (or 0/1), Hamming, and the example-based F-measure. To make a fair comparison, we implemented three ML learners for optimizing explicitly each one of these measures in a common framework. This can be done considering a subset of labels as a structured output. Then, we use structured output support vector machines tailored to optimize a given loss function. The paper includes an exhaustive experimental comparison. The conclusion is that in most cases, the optimization of the Hamming loss produces the best or competitive scores. This is a practical result since the Hamming loss can be minimized using a bunch of binary classifiers, one for each label separately, and therefore, it is a scalable and fast method to learn ML tasks. Additionally, we observe that in noise-free learning tasks optimizing the subset loss is the best option, but the differences are very small. We have also noticed that the biggest room for improvement can be found when the goal is to optimize an F-measure in noisy learning tasks.

Building patterns are important settlement structures in applications like automated generalization and spatial data mining. Previous investigations have focused on a few types of building patterns (e.g. collinear building alignments); while many other types are less discussed. In order to get better known of the building patterns available in geography, this paper studies existing topographic maps at large to medium scales, and proposes and discusses a comprehensive typology of building patterns, their distinctions and characteristics. The proposed typology includes linear alignments (i.e. collinear, curvilinear, align-along-road alignments) and nonlinear clusters (grid-like and unstructured patterns). We concentrate in this paper on two specific building structures: align-along-road alignment and unstructured clusters. Two graph-theoretic algorithms are presented to detect these two types of building patterns. The approach bases itself on auxiliary data structures such as Delaunay triangulation and minimum spanning trees for clustering; several rules are used to refine the clusters into specific building patterns. Finally, the proposed algorithms are tested against a real topographic dataset of the Netherlands, which shows the potential of the two algorithms.

The relationship between generalization and solutions functional complexity in genetic programming (GP) has been recently investigated. Three main contributions are contained in this paper: (1) a new measure of functional complexity for GP solutions, called Graph Based Complexity (GBC) is defined and we show that it has a higher correlation with GP performance on out-of-sample data than another complexity measure introduced in a recent publication. (2) A new measure is presented, called Graph Based Learning Ability (GBLA). It is inspired by the GBC and its goal is to quantify the ability of GP to learn "difficult" training points; we show that GBLA is negatively correlated with the performance of GP on out-of-sample data. (3) Finally, we use the ideas that have inspired the definition of GBC and GBLA to define a new fitness function, whose suitability is empirically demonstrated. The experimental results reported in this paper have been obtained using three real-life multidimensional regression problems.

In this paper we show how it is possible to construct an efficient Migration models in the study of credit risk problems presented in Jarrow et al. (Rev Financ Stud 10:481–523, 1997) with Markov environment. Recently it was introduced the semi-Markov process in the migration models (D’Amico et al. Decis Econ Finan 28:79–93, 2005a). The introduction of semi-Markov processes permits to overtake some of the Markov constraints given by the dependence of transition probabilities on the duration into a rating category. In this paper, it is shown how it is possible to take into account simultaneously backward and forward processes at beginning and at the end of the time in which the credit risk model is observed. With such a generalization, it is possible to consider what happens inside the time after the first transition and before the last transition where the problem is studied. This paper generalizes other papers presented before. The model is presented in a discrete time environment.

Alamdari and Shmoys introduced the following variant of the k-median problem. In this variant, we are given an instance of the k-median problem and a threshold value. Then this variant is the same as the k-median problem except that if the distance between a client i and a facility j is more than the threshold value, then i is not allowed to be connected to j. In this paper, we consider a matroid generalization of this variant of the k-median problem. First, we introduce a generalization of this variant in which the constraint on the number of opened facilities is replaced by a matroid constraint. Then we propose a polynomial-time bicriteria approximation algorithm for this problem by combining the algorithm of Alamdari and Shmoys and the algorithm of Krishnaswamy, Kumar, Nagarajan, Sabharwal, and Saha for a matroid generalization of the k-median problem.

The "quantum complexity" of a unitary operator measures the difficulty of its construction from a set of elementary quantum gates. While the notion of quantum complexity was first introduced as a quantum generalization of the classical computational complexity, it has since been argued to hold a fundamental significance in its own right, as a physical quantity analogous to the thermodynamic entropy. In this paper, we present a unified perspective on various notions of quantum complexity, viewed as functions on the space of unitary operators. One striking feature of these functions is that they can exhibit non-smooth and even fractal behaviour. We use ideas from Diophantine approximation theory and sub-Riemannian geometry to rigorously quantify this lack of smoothness. Implications for the physical meaning of quantum complexity are discussed., Comment: v2: minor revisions, remarks added on difference between nilpotent and unitary groups from a complexity viewpoint. 10 pages, 1 figure

In this paper we derive from field theory a L\"uscher-formula, which gives the leading exponentially small in volume corrections to the 1-particle form-factors in non-diagonally scattering integrable quantum field theories. Our final formula is expressed in terms of appropriate expressions of 1- and 3-particle form-factors, and can be considered as the generalization of previous results obtained for diagonally scattering bosonic integrable quantum field theories. Since our formulas are also valid for fermions and operators with non-zero Lorentz-spin, we demonstrated our results in the Massive Thirring Model, and checked our formula against 1-loop perturbation theory finding perfect agreement., Comment: 28 pages

The Non-Uniform $k$-center (NUkC) problem has recently been formulated by Chakrabarty, Goyal and Krishnaswamy [ICALP, 2016] as a generalization of the classical $k$-center clustering problem. In NUkC, given a set of $n$ points $P$ in a metric space and non-negative numbers $r_1, r_2, \ldots , r_k$, the goal is to find the minimum dilation $\alpha$ and to choose $k$ balls centered at the points of $P$ with radius $\alpha\cdot r_i$ for $1\le i\le k$, such that all points of $P$ are contained in the union of the chosen balls. They showed that the problem is NP-hard to approximate within any factor even in tree metrics. On the other hand, they designed a "bi-criteria" constant approximation algorithm that uses a constant times $k$ balls. Surprisingly, no true approximation is known even in the special case when the $r_i$'s belong to a fixed set of size 3. In this paper, we study the NUkC problem under perturbation resilience, which was introduced by Bilu and Linial [Combinatorics, Probability and Computing, 2012]. We show that the problem under 2-perturbation resilience is polynomial time solvable when the $r_i$'s belong to a constant sized set. However, we show that perturbation resilience does not help in the general case. In particular, our findings imply that even with perturbation resilience one cannot hope to find any "good" approximation for the problem., Comment: 20 pages, 5 figures

Many real-world problems have complicated objective functions whose optimization requires sophisticated sequential decision-making strategies. Modelling human function learning has been the subject of intense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. The Gaussian Process based Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper we focus on Bayesian Optimization and analyse experimentally how it compares to humans while searching for the maximum of an unknown 2D function. A set of controlled experiments with 53 subjects confirm that Gaussian Processes provide a general model to explain different patterns of learning enabled search and optimization in humans.

© 2020, Springer Nature Switzerland AG. Real-time traffic planning and scheduling optimization are critical for developing low-cost, reliable, resilient, and efficient transport systems. In this paper, we present a real-time application that uses machine learning techniques to forecast the train carriage load when a train departure from a platform. With the predicted carriage load, crew can efficiently manage passenger flow, improving the efficiency of boarding and alighting, and thereby reducing the time trains spend at stations. We developed the application in collaboration with Sydney Trains. Most data are publicly available on Open Data Hub, which is supported by the Transport for NSW. We investigated the performance of different models, features, and measured their contributions to prediction accuracy. From this we propose a novel learning strategy, called Multi-Stream Learning, which merges streams having similar concept drift patterns to boost the training data size with the aim of achieving lower generalization errors. We have summarized our solutions and hope researchers and industrial users who might be facing similar problems will benefit from our findings.

Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in which classifier parameters shared across domains are modeled as distributions. This enables better handling of prediction uncertainty on unseen domains. To deal with domain shift, we learn domain-invariant representations by the proposed principle of meta variational information bottleneck, we call MetaVIB. MetaVIB is derived from novel variational bounds of mutual information, by leveraging the meta-learning setting of domain generalization. Through episodic training, MetaVIB learns to gradually narrow domain gaps to establish domain-invariant representations, while simultaneously maximizing prediction accuracy. We conduct experiments on three benchmarks for cross-domain visual recognition. Comprehensive ablation studies validate the benefits of MetaVIB for domain generalization. The comparison results demonstrate our method outperforms previous approaches consistently.

The paper studies a differential game played by two competing firms over a finite time horizon. As the game progresses, the firms observe the position of the game, i.e., the current time and the current market shares. Each firm uses pricing and advertising in order to influence market shares. We suggest a generalization of the Lanchester market share dynamics such that the rates at which firms attract market share from each other are determined not only by their advertising efforts but also by the consumer prices charged in the market. A full characterization of Nash equilibrium price and advertising strategies is obtained.

In this paper, possible scenarios within the experimental dynamic response of a vibro-impact single-degree-of-freedom system, symmetrically constrained by deformable and dissipative bumpers, were identified and described. The different scenarios were obtained varying selected parameters, namely peak table acceleration $$\hbox {A}$$ , amplitude of the total gap between mass and bumpers $$\hbox {G}$$ and bumper’s stiffness $$\hbox {B}$$ . Subsequently, using a Simplified Nonlinear Model results in good agreement with the experimental outcomes were obtained, although the model includes only the nonlinearities due to clearance existence and impact occurrence. Further numerical analysis highlighted other scenarios that can be obtained for values of the parameters not considered in the experimental laboratory campaign. Finally, to attempt a generalization of the results, suitable dimensionless parameters were introduced.

Using network control structures this paper introduces network communication games as a generalization of vertex games and edge games corresponding to communication situations and studies their decomposition into unanimity games. We obtain a relation between the dividends of the network communication game and the underlying transferable utility game, which depends on the structure of the undirected graph. This relation extends the computational results for tree communication networks to general undirected graphs and is used to derive new characterizations of the Myerson value and the position value. Moreover, network communication games also allow to consider both the vertices and the edges of the graph as players, leading to a new network value.

The multi variate mixture dynamics model is a tractable, dynamical, arbitrage-free multivariate model characterized by transparency on the dependence structure, since closed form formulae for terminal correlations, average correlations and copula function are available. It also allows for complete decorrelation between assets and instantaneous variances. Each single asset is modelled according to a lognormal mixture dynamics model, and this univariate version is widely used in the industry due to its flexibility and accuracy. The same property holds for the multivariate process of all assets, whose density is a mixture of multivariate basic densities. This allows for consistency of single asset and index/portfolio smile. In this paper, we generalize the MVMD model by introducing shifted dynamics and we propose a definition of implied correlation under this model. We investigate whether the model is able to consistently reproduce the implied volatility of FX cross rates once the single components are calibrated to univariate shifted lognormal mixture dynamics models. We consider in particular the case of the Chinese Renminbi FX rate, showing that the shifted MVMD model correctly recovers the CNY/EUR smile given the EUR/USD smile and the USD/CNY smile, thus highlighting that the model can also work as an arbitrage free volatility smile extrapolation tool for cross currencies that may not be liquid or fully observable. We compare the performance of the shifted MVMD model in terms of implied correlation with those of the shifted simply correlated mixture dynamics model where the dynamics of the single assets are connected naively by introducing correlation among their Brownian motions. Finally, we introduce a model with uncertain volatilities and correlation. The Markovian projection of this model is a generalization of the shifted MVMD model.

Producción Científica, The Winsorized mean is a well-known robust estimator of the population mean. It can also be seen as a symmetric aggregation function (in fact, it is an ordered weighted averaging operator), which means that the information sources (for instance, criteria or experts’ opinions) have the same importance. However, in many practical applications (for instance, in many multiattribute decision making problems) it is necessary to consider that the information sources have different importance. For this reason, in this paper we propose a natural generalization of the Winsorized means so that the sources of information can be weighted differently. The new functions, which we will call Winsorized weighted means, are a specific case of the Choquet integral and they are analyzed through several indices for which we give closed-form expressions: the orness degree, k-conjunctiveness and k-disjunctiveness indices, veto and favor indices, Shapley values and interaction indices. We also provide a closed-form expression for the Möbius transform and we show how we can aggregate data so that each information source has the desired weighting and outliers have no influence in the aggregated value., Este trabajo forma parte del proyecto de investigación: MEC-FEDER Grant ECO2016-77900-P

In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations I,i¾?J such that $$J=T_PI$$, where $$T_P$$ is the immediate consequence operator, we infer the program P. Here we propose a new learning approach that is more efficient in terms of output quality. This new approach relies on specialization in place of generalization. It generates hypotheses by specialization from the most general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. The new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.

WOS: 000422933800017 In recent years, soft sets and neutrosophic sets have become a subject of great interest for researchers and have been widely studied based on decision-making problems. In this paper, we propose a new concept of the soft sets that is called interval-valued neutrosophic parameterized interval-valued neutrosophic soft sets (ivnpivn-soft sets). It is a generalization of the other soft sets such as fuzzy soft sets, intuitionistic fuzzy soft sets, neutrosophic soft sets, fuzzy parameterized soft sets, intuitionistic fuzzy parameterized soft sets, neutrosophic parameterized neutrosophic soft sets. Also, we proposed ivnpivn-soft matrices which are representative of the ivnpivn-soft sets. We then developed a decision-making method on the ivnpivn-soft sets and ivnpivn-soft matrices. Then, we proposed a numerical example to verify validity and feasibility of the developed method. Finally, the proposed method is compared with several different methods to verify its feasibility.

Trabajo presentado a la 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018. Cádiz, 11-15 junio 2018 This paper introduces a generalization of overlap functions by extending one of the boundary conditions of its definition. More specifically, instead of requiring that 'the considered function is equal to zero if and only if some of the inputs is equal to zero' , we allow the range in which some t-norm is zero. We call such generalization by a t-overlap function with respect to such t-norm. Then we analyze the main properties of t-overlap function and introduce some construction methods. Supported by Caixa and Fundacion Caja Navarra of Spain, the Brazilian National Counsel of Technological and Scientific Development CNPq (Proc. 307781/2016-0), the Spanish Ministry of Science and Technology (TIN2016-77356-P).

In this paper we consider the problem of simultaneously eliciting the preferences of a group of users in an interactive way. We focus on constructive recommendation tasks, where the instance to be recommended should be synthesized by searching in a constrained configuration space rather than choosing among a set of pre-determined options. We adopt a setwise max-margin optimization method, that can be viewed as a generalization of max-margin learning to sets, supporting the identification of informative questions and encouraging sparsity in the parameter space. We extend setwise max-margin to multiple users and we provide strategies for choosing the user to be queried next and identifying an informative query to ask. At each stage of the interaction, each user is associated with a set of parameter weights (a sort of alternative options for the unknown user utility) that can be used to identify “similar” users and to propagate preference information between them. We present simulation results evaluating the effectiveness of our procedure, showing that our approach compares favorably with respect to straightforward adaptations in a multi-user setting of elicitation methods conceived for single users. ispartof: pages:3-17 ispartof: Proceedings of the 5th International Conference on Algorithmic Decision Theory (ADT17) vol:10576 pages:3-17 ispartof: International Conference on Algorithmic Decision Theory status: published

In this paper we consider a generalization of M/GI/N/\(\infty \) queues, in which customer capacity is an additional parameter of the system and it is independent of the service time. In more detail we focus on the distributions of the total capacity of customers in the different elements of the queue (waiting line, service and entire system) and provide approximate expressions for the corresponding characteristic functions. To verify the goodness of the proposed approximation, several sets of simulations have been carried out, considering discrete and continuous distributions of the customer capacity and using the Kolmogorov distance as a measure of similarity.

This paper reports adult and child knowledge of the generalized scalar implicature (GCI) of disjunction, the non-scalar ‘Allover’ GCI and the particularized no-contrast implicature. The contributions of scales, generalization, and relational complexity to the developmental difficulty of phenomena at the semantic–pragmatic interface are discussed. Results show that children as old as 9 years do not demonstrate adultlike knowledge of the scalar GCI of disjunction or the no-contrast PCI, while the ‘Allover’ GCI is demonstrated at 5 years. We conclude that the quaternary level relational complexity of the later developing implicature and the ternary level complexity of the earlier developing implicature, as analyzed by Halford et al. (1998) Relational Complexity Metric, can account for this developmental pattern, and not scales or generality.

The method of Proper Orthogonal Decompositions (POD) is a data-based method that is suitable for the reduction of large-scale distributed systems. In this paper we propose a generalization of the POD method so as to take the ND nature of a distributed model into account. This results in a novel procedure for model reduction of systems with multiple independent variables. Data in multiple independent variables is associated with the mathematical structure of a tensor. We show how orthonormal decompositions of this tensor can be used to derive suitable projection spaces. These projection spaces prove useful for determining reduced order models by performing Galerkin projections on equation residuals. We demonstrate how prior knowledge about the structure of the model reduction problem can be used to improve the quality of approximations. The tensor decomposition techniques are demonstrated on an application in data compression. The proposed model reduction procedure is illustrated on a heat diffusion problem.

In this paper, we used an analytical method to calculate the effects that produce the parameter’s fluctuations characterizing a generalization of Nagumo model. (The extinction option is replaced by one of low density homogeneous population.) Moreover, we also check the results by means of numerical simulations of the corresponding stochastic process. We find that these fluctuations have a strong impact on the solutions producing interesting changes. Fil: Mangioni, Sergio Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata; Argentina

Although off-the-shelf clustering algorithms, such as those based on spectral graph theory, do a pretty good job at finding clusters of arbitrary shape and structure, they are inherently unable to satisfactorily deal with situations involving the presence of cluttered backgrounds. On the other hand, dominant sets, a generalization of the notion of maximal clique to edge-weighted graphs, exhibit a complementary nature: they are remarkably effective in dealing with background noise but tend to favor compact groups. In order to take the best of the two approaches, in this paper we propose to combine path-based similarity measures, which exploit connectedness information of the elements to be clustered, with the dominant-set approach. The resulting algorithm is shown to consistently outperform standard clustering methods over a variety of datasets under severe noise conditions.

We introduce a generalization of the graph isomorphism problem. Given two graphs G 1 = (V 1, E 1) and G 2 = (V 2, E 2) and two integers l and d, we seek for a one-to-one mapping f:V 1 → V 2, such that for every v ∈ V 1, it holds that L′ v − L v ≤ d, where Open image in new window , Open image in new window , and \(N^i_{G}(v)\) denotes the set of vertices which have distance at most i to v in a graph G. We call this problem Compactness-Preserving Mapping (CPM). In the paper we study CPM with input graphs being trees and present a dichotomy of classical complexity with respect to different values of l and d. CPM on trees can be solved in polynomial time only if l ≤ 2 and d ≤ 1.

The concept of negative temperatures has occasionally been used in connection with quantum systems. A recent example of this sort is reported in the paper of S. Braun et al. [Science 339,52 (2013)], where an attractively interacting ensemble of ultracold atoms is investigated experimentally and found to correspond to a negative-temperature system since the entropy decreases with increasing energy at the high end of the energy spectrum. As the authors suggest, it would be of interest to investigate whether a suitable generalization of standard cosmological theory could be helpful, in order to elucidate the observed accelerated expansion of the universe usually explained in terms of a positive tensile stress (negative pressure). In the present note we take up this basic idea and investigate a generalization of the standard viscous cosmological theory, not by admitting negative temperatures but instead by letting the bulk viscosity take negative values. Evidently, such an approach breaks standard thermodynamics, but may actually be regarded to lead to the same kind of bizarre consequences as the standard approach of admitting the equation-of-state parameter w to be less than -1. In universe models dominated by negative viscosity we find that the fluid's entropy decreases with time, as one would expect. Moreover, we find that the fluid transition from the quintessence region into the phantom region (thus passing the phantom divide w=-1) can actually be reversed. Also in generalizations of the LCDM-universe models with a fluid having negative bulk viscosity we find that the viscosity decreases the expansion of the universe., Comment: 7 pages latex, no figures, published electronically in Astrophys. Space Sci

Moraglio et al. have recently introduced new genetic operators for genetic programming, called geometric semantic operators. These operators induce a unimodal fitness landscape for all the problems consisting in matching input data with known target outputs (like regression and classification). This feature facilitates genetic programming evolvability, which makes these operators extremely promising. Nevertheless, Moraglio et al. leave open problems, the most important one being the fact that these operators, by construction, always produce offspring that are larger than their parents, causing an exponential growth in the size of the individuals, which actually renders them useless in practice. In this paper we overcome this limitation by presenting a new efficient implementation of the geometric semantic operators. This allows us, for the first time, to use them on complex real-life applications, like the two problems in pharmacokinetics that we address here. Our results confirm the excellent evolvability of geometric semantic operators, demonstrated by the good results obtained on training data. Furthermore, we have also achieved a surprisingly good generalization ability, a fact that can be explained considering some properties of geometric semantic operators, which makes them even more appealing than before. © 2013 Springer-Verlag.

In this paper we study the minimal decomposition of octilinear polygons with holes into octilinear triangles and rectangles. This new problem is relevant in the context of modern electronic CAD systems, where it arises when the generation and propagation of electromagnetic noise into multi-layer PCBs has to be detected. It can be seen as a generalization of a problem deeply investigated in the last decades: the minimal decomposition of rectilinear polygons into rectangles, which is solvable in polynomial time. We show that the new problem is NP-hard. We also show the NP-hardness of a related problem, that is the decomposition of an octilinear polygon with holes into octilinear convex polygons. For both problems, we propose efficient approximation algorithms.

International audience; This paper describes a generalization of the LowerUnits algorithm for the compression of propositional resolution proofs. The generalized algorithm, here called LowerUnivalents, is able to lower not only units but also subproofs of non-unit clauses, provided that they satisfy some additional conditions. This new algorithm is particularly suited to be combined with the RecyclePivotsWithIntersection algorithm. A formal proof that LowerUnivalents always compresses more than LowerUnits is shown, and both algorithms are empirically compared on thousands of proofs produced by the SMT-Solver veriT.

This paper studies the Fermi-Pasta-Ulam problem having in mind the generalization provided by Fractional Calculus (FC). The study starts by addressing the classical formulation, based on the standard integer order differential calculus and evaluates the time and frequency responses. A first generalization to be investigated consists in the direct replacement of the springs by fractional elements of the dissipative type. It is observed that the responses settle rapidly and no relevant phenomena occur. A second approach consists of replacing the springs by a blend of energy extracting and energy inserting elements of symmetrical fractional order with amplitude modulated by quadratic terms. The numerical results reveal a response close to chaotic behaviour.

A natural generalization of the well known generalized linear models is to allow only for some of the predictors to be modeled linearly while others are modeled nonparametrically. However, this model can face the so called “curse of dimensionality” problem that can be solved by imposing a nonparametric dependence on some unknown projection of the carriers. More precisely, we assume that the observations (yi,xi,ti), 1≤i≤n, are such that ti∈ℝq, xi∈ℝp and yi|(xi,ti)∼F(⋅,μi) with μi=H(η(αTti)+xTiβ)μi=H(η(αTti)+xiTβ) , for some known distribution function F and link function H. The function η:ℝ→ℝ and the parameters α and β are unknown and to be estimated. This model is known as the generalized partly linear single-index model. In this paper, we introduce a family of robust estimates for the parametric and nonparametric components under a generalized partially linear single-index model. It is shown that the estimates of α and β are root-n consistent and asymptotically normally distributed. Through a Monte Carlo study, we compare the performance of the proposed estimators with that of the classical ones. Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina Fil: Rodriguez, Daniela Andrea. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina

Empirical models to correlate deformational modulus along with petrographic features which are intrinsic and inherent properties of rock with other basic mechanical and physical properties have earlier been proposed with experiential and assumed reasoning. However, in most cases, such empirical models make certain basic assumptions and hence bring in a degree of dispose and doubt. An attempt has been made in this paper to analyze and compare the efficiency and applicability of different cognitive algorithms for the prediction of deformational modulus and texture coefficient. The importance of knowledge of deformational modulus is unparalleled with the view to the operational difficulties in its determination. Rock samples were taken from a tectonically active and complex sequence from a large underground excavation in the Himalayan region and were tested in the laboratory to determine the different strength properties. One hundred and seventy six rock samples test results were used as part of the experiment. The uniaxial compressive strength, tensile strength, axial point load strength, porosity, and void ratio were taken as inputs to get deformational modulus and texture coefficient. Networks were trained to optimum number of epochs or iterations to make suitable prediction. The results of intelligent systems have been tested against that of statistical methods as a test of precision of the model in generalization principles.

Weakly acyclic games form a natural generalization of the class of games that have the finite improvement property (FIP). In such games one stipulates that from any initial joint strategy some finite improvement path exists. We classify weakly acyclic games using the concept of a scheduler introduced in arXiv:1202.2209. We also show that finite games that can be solved by the iterated elimination of never best response strategies are weakly acyclic. Finally, we explain how the schedulers allow us to improve the bounds on finding a Nash equilibrium in a weakly acyclic game., Comment: 25 pages. To appear in Theory and Decision. A preliminary version of this paper appeared in Proc. 5th International Symposium on Algorithmic Game Theory, (SAGT 2012). Lecture Notes in Computer Science 7615, Springer, pp. 1-12

In this paper we present 'Quantum Model Theory' (QMod), a theory we developed to model entities that entail the typical quantum effects of 'contextuality', 'superposition', 'interference', 'entanglement' and 'emergence'. The aim of QMod is to put forward a theoretical framework that is more general than standard quantum mechanics, in the sense that, for its complex version it only uses this quantum calculus locally, i.e. for each context corresponding to a measurement, and for its real version it does not need the property of 'linearity of the set of states' to model the quantum effect. In this sense, QMod is a generalization of quantum mechanics, similar to how the general relativity manifold mathematical formalism is a generalization of special relativity. We prove by means of a representation theorem that QMod can be used for any entity entailing the typical quantum effects mentioned above. Some examples of application of QMod in concept theory and macroscopic physics are also considered., Comment: 1 figure

In this paper a complex-order van der Pol oscillator is considered. The complex derivative $D^{\alpha\pm\jmath\beta}$ , with α,β∈R + is a generalization of the concept of integer derivative, where α=1, β=0. By applying the concept of complex derivative, we obtain a high-dimensional parameter space. Amplitude and period values of the periodic solutions of the two versions of the complex-order van der Pol oscillator are studied for variation of these parameters. Fourier transforms of the periodic solutions of the two oscillators are also analyzed.

In this paper, we focus on protein contact map prediction. We describe a method where contact maps are predicted using decision tree-based model. The algorithm includes the subsequence information between the couple of analyzed amino acids. In order to evaluate the method generalization capabilities, we carry out an experiment using 173 non-homologous proteins of known structures. Our results indicate that the method can assign protein contacts with an average accuracy of 0.34, superior to the 0.25 obtained by the FNETCSS method. This shows that our algorithm improves the accuracy with respect to the methods compared, especially with the increase of protein length

In this paper we consider linear operators of the form L+ λI between suitable functions spaces, when 0 is an eigenvalue of L with constant associated eigenfunctions.We introduce a new notion of “quasi”-uniform maximum principle, named k-uniform maximum principle, which holds for ? belonging to certain neighborhoods of 0 depending on k \(\epsilon\, \mathbf{R}\) +. Our approach actually also covers the case of a “quasi”-uniform antimaximum principle, and is based on an L∞ - L2 estimate. As an application, we prove some generalization of known results for elliptic Neumann problems and new results for parabolic problems with time-periodic boundary conditions.

One major idea in structured prediction is to assume that the predictor computes its output by finding the maximum of a score function. The training of such a predictor can then be cast as the problem of finding weights of the score function so that the output of the predictor on the inputs matches the corresponding structured labels on the training set. A similar problem is studied in inverse reinforcement learning (IRL) where one is given an environment and a set of trajectories and the problem is to find a reward function such that an agent acting optimally with respect to the reward function would follow trajectories that match those in the training set. In this paper we show how IRL algorithms can be applied to structured prediction, in particular to parser training. We present a number of recent incremental IRL algorithms in a unified framework and map them to parser training algorithms. This allows us to recover some existing parser training algorithms, as well as to obtain a new one. The resulting algorithms are compared in terms of their sensitivity to the choice of various parameters and generalization ability on the Penn Treebank WSJ corpus.

We study three dimensional volumes of higher order tensors, using Finsler geometry. The application considered here is in medical image analysis, specifically High Angular Resolution Diffusion Imaging (HARDI) [1] of the brain. We want to find robust ways to reveal the architecture of the neural fibers in brain white matter. In Diffusion Tensor Imaging (DTI), the diffusion of water is modeled with a symmetric positive definite second order tensor, based on the assumption that there exists one dominant direction of fibers restricting the thermal motion of water molecules, leading naturally to a Riemannian framework. HARDI may potentially overcome the shortcomings of DTI by allowing multiple relevant directions, but invalidates the Riemannian approach. Instead Finsler geometry provides the natural geometric generalization appropriate for multi-fiber analysis. In this paper we provide the exact criterion to determine whether a field of spherical functions has a Finsler structure. We also show a fiber tracking method in Finsler setting. Our model also incorporates a scale parameter, which is beneficial in view of the noisy nature of the data. We demonstrate our methods on analytic as well as real HARDI data.

This paper introduces "twisted Edwards curves," a generalization of the recently introduced Edwards curves; shows that twisted Edwards curves include more curves over finite fields, and in particular every elliptic curve in Montgomery form; shows how to cover even more curves via isogenies; presents fast explicit formulas for twisted Edwards curves in projective and inverted coordinates; and shows that twisted Edwards curves save time for many curves that were already expressible as Edwards curves.

In this paper generalized Fischer spaces are defined and some results on their classification are obtained.

The Node Weighted Steiner Tree Problem (NW-STP) is a generalization of the Steiner Tree Problem. A lagrangean heuristic presented in EngevallS: StrLBN: 98, and based on the work in Lucena: 92, solves the problem by relaxing an exponential family of generalized subtour elimination constraints and taking into account only the violated ones as the computation proceeds. In EngevallS: StrLBN: 98 the computational results refer to complete graphs up to one hundred vertices. In this paper, we present a branch-and-bound algorithm based on this formulation. Its performance on the instances from the literature confirms the effectiveness of the approach. The experimentation on a newly generated set of benchmark problems, more similar to the real-world applications, shows that the approach is still valid, provided that suitable refinements on the bounding procedures and a preprocessing phase are introduced. The algorithm solves to optimality all of the considered instances up to one thousand vertices, with the exception of 11 hard instances, derived from the literature of a similar problem, the Prize Collecting Steiner Tree Problem.

C bounded model checking (cbmc) has proved to be a successful approach to automatic software analysis. The key idea is to (i) build a propositional formula whose models correspond to program traces (of bounded length) that violate some given property and (ii) use state-of-the-art SAT solvers to check the resulting formulae for satisfiability. In this paper, we propose a generalisation of the cbmc approach on the basis of an encoding into richer (but still decidable) theories than propositional logic. We show that our approach may lead to considerably more compact formulae than those obtained with cbmc. We have built a prototype implementation of our technique that uses a satisfiability modulo theories (SMT) solver to solve the resulting formulae. Computer experiments indicate that our approach compares favourably with—and on some significant problems outperforms—cbmc.

The Local Closed-World Assumption (LCWA) is a generalization of Reiter's Closed-World Assumption (CWA) for relational databases that may be incomplete. Two basic questions that are related to this assumption are: (1) how to represent the fact that only part of the information is known to be complete, and (2) how to properly reason with this information, that is: how to determine whether an answer to a database query is complete even though the database information is incomplete. In this paper we concentrate on the second issue based on a treatment of the first issue developed in earlier work of the authors. For this we consider a fixpoint semantics for declarative theories that represent locally complete databases. This semantics is based on 3-valued interpretations that allow to distinguish between the certain and possible consequences of the database's theory. © Springer-Verlag Berlin Heidelberg 2006. ispartof: pages:407-421 ispartof: Lecture notes in computer science vol:4246 pages:407-421 ispartof: 13th International Conference on Logic for Programming Artificial Intelligence and Reasoning location:Phnom Penh., Cambodia date:13 Nov - 17 Nov 2006 status: published